Artificial intelligence (AI) has shown increasing potential in assisting users with decision-making. However, the impact of AI decision speed on users' adoption intention has received limited attention compared to the focus on decision quality. Building on cue utilization theory, this study investigates the influence of AI decision speed on users' intention to adopt AI. Three experiments were conducted, revealing that users exhibit a higher intention to adopt AI when AI's decision speed is higher and historical decision quality is better. Furthermore, the perceived intelligence and perceived risk in decision-making act as mediating variables in these effects. Importantly, the study finds that historical decision quality moderates the relationship between AI decision speed and user adoption, weakening the impact in conditions of high quality. These findings contribute to the understanding of AI adoption and offer practical implications for AI service providers and developers.


Paper Number 1213; Track Human; Complete Paper


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